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Multivariate statistics have been used in this pilot study.
Many authors had worked on the diagnostic standardization of many other diseases, and various techniques of multivariate statistics have been applied in the construction of diagnostic models in TCM, such as discriminant analysis and regression analysis in the diagnosis of blood stasis syndrome [ 4, 5] and stroke [ 6, 7].
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In this paper, an automated image analysis strategy that combines classical image analysis techniques with multivariate statistics has been developed for online analysis of in situ images from crystallization process.
Although multivariate statistics has some superiority in the solution of quantitative diagnosis in TCM, the problem on clinical data analysis with high nonlinearity could not be solved by these techniques.
A typical case in practice is to assume (or show) that the test statistics have a joint multivariate normal distribution with non-negative correlations.
Secondly, for a multivariate normal distribution, only the second-order statistics have to be determined.
Descriptive statistics have been applied.
These statistics have different sensitivities.
In spite of this heavy parasitic burden, a salient feature emerged from multivariate statistics: that parasite infection has, at the global level, an apparent marginal impact (only 2.4% of the transcripts were found modulated) on the expression profile of infected MΦs.
(vii) We have applied multivariate statistics for the analysis of factors affecting mRNA decay rates, which identifies direct effects and distinguishes them from mere correlations.
In this approach, large data sets of chemical descriptors are used for characterizing chemicals, and multivariate statistics is used to extract those chemical descriptors that have the highest predictive values for odor responses of individual receptors or receptor families.
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